MétaCan
Menu
Back to cohort
Record W1607607431 · doi:10.1371/journal.pone.0129629

Pseudomonas aeruginosa MifS-MifR Two-Component System Is Specific for α-Ketoglutarate Utilization

2015· article· en· W1607607431 on OpenAlexfundno aff
Gorakh Tatke, Hansi Kumari, Eugenia Silva‐Herzog, Lourdes Ramirez, Kalai Mathee

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial biofilms and quorum sensing
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyNational Institute of Allergy and Infectious DiseasesHerbert Wertheim College of Medicine, Florida International UniversityNational Institute of General Medical SciencesDirectorate for Biological SciencesUniversité de LausanneFlorida International UniversityNational Science FoundationNational Institutes of HealthConcordia University
KeywordsOperonBiologyCitric acid cycleMutantPseudomonas aeruginosaVirulenceTwo-component regulatory systemBiofilmMicrobiologySigma factorBiochemistryGeneBacteriaGeneticsMetabolismEscherichia coli

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa is a Gram-negative, metabolically versatile opportunistic pathogen that elaborates a multitude of virulence factors, and is extraordinarily resistant to a gamut of clinically significant antibiotics. This ability, in part, is mediated by two-component regulatory systems (TCS) that play a crucial role in modulating virulence mechanisms and metabolism. MifS (PA5512) and MifR (PA5511) form one such TCS implicated in biofilm formation. MifS is a sensor kinase whereas MifR belongs to the NtrC superfamily of transcriptional regulators that interact with RpoN (σ54). In this study we demonstrate that the mifS and mifR genes form a two-gene operon. The close proximity of mifSR operon to poxB (PA5514) encoding a ß-lactamase hinted at the role of MifSR TCS in regulating antibiotic resistance. To better understand this TCS, clean in-frame deletions were made in P. aeruginosa PAO1 creating PAO∆mifS, PAO∆mifR and PAO∆mifSR. The loss of mifSR had no effect on the antibiotic resistance profile. Phenotypic microarray (BioLOG) analyses of PAO∆mifS and PAO∆mifR revealed that these mutants were unable to utilize C5-dicarboxylate α-ketoglutarate (α-KG), a key tricarboxylic acid cycle intermediate. This finding was confirmed using growth analyses, and the defect can be rescued by mifR or mifSR expressed in trans. These mifSR mutants were able to utilize all the other TCA cycle intermediates (citrate, succinate, fumarate, oxaloacetate or malate) and sugars (glucose or sucrose) except α-KG as the sole carbon source. We confirmed that the mifSR mutants have functional dehydrogenase complex suggesting a possible defect in α-KG transport. The inability of the mutants to utilize α-KG was rescued by expressing PA5530, encoding C5-dicarboxylate transporter, under a regulatable promoter. In addition, we demonstrate that besides MifSR and PA5530, α-KG utilization requires functional RpoN. These data clearly suggests that P. aeruginosa MifSR TCS is involved in sensing α-KG and regulating its transport and subsequent metabolism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.096
GPT teacher head0.257
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicBacterial biofilms and quorum sensingFrench-language works237,207